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Statistics of Knots, Geometry of Conformations, and Evolution of Proteins

Overview of attention for article published in PLoS Computational Biology, May 2006
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Title
Statistics of Knots, Geometry of Conformations, and Evolution of Proteins
Published in
PLoS Computational Biology, May 2006
DOI 10.1371/journal.pcbi.0020045
Pubmed ID
Authors

Rhonald C Lua, Alexander Y Grosberg

Abstract

Like shoelaces, the backbones of proteins may get entangled and form knots. However, only a few knots in native proteins have been identified so far. To more quantitatively assess the rarity of knots in proteins, we make an explicit comparison between the knotting probabilities in native proteins and in random compact loops. We identify knots in proteins statistically, applying the mathematics of knot invariants to the loops obtained by complementing the protein backbone with an ensemble of random closures, and assigning a certain knot type to a given protein if and only if this knot dominates the closure statistics (which tells us that the knot is determined by the protein and not by a particular method of closure). We also examine the local fractal or geometrical properties of proteins via computational measurements of the end-to-end distance and the degree of interpenetration of its subchains. Although we did identify some rather complex knots, we show that native conformations of proteins have statistically fewer knots than random compact loops, and that the local geometrical properties, such as the crumpled character of the conformations at a certain range of scales, are consistent with the rarity of knots. From these, we may conclude that the known "protein universe" (set of native conformations) avoids knots. However, the precise reason for this is unknown--for instance, if knots were removed by evolution due to their unfavorable effect on protein folding or function or due to some other unidentified property of protein evolution.

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Geographical breakdown

Country Count As %
Germany 3 3%
United States 3 3%
Italy 1 <1%
United Kingdom 1 <1%
South Africa 1 <1%
Japan 1 <1%
Mexico 1 <1%
Unknown 91 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 18%
Student > Ph. D. Student 16 16%
Student > Master 12 12%
Student > Bachelor 11 11%
Professor > Associate Professor 11 11%
Other 22 22%
Unknown 12 12%
Readers by discipline Count As %
Physics and Astronomy 28 27%
Agricultural and Biological Sciences 18 18%
Chemistry 17 17%
Biochemistry, Genetics and Molecular Biology 8 8%
Engineering 8 8%
Other 9 9%
Unknown 14 14%